Overview

Dataset statistics

Number of variables25
Number of observations1334
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory251.6 KiB
Average record size in memory193.1 B

Variable types

Numeric21
Categorical3
Boolean1

Warnings

Credit Limit is highly correlated with Outstanding/Sept05 and 5 other fieldsHigh correlation
PayStat/Sept05 is highly correlated with PayStat/Aug05High correlation
PayStat/Aug05 is highly correlated with PayStat/Sept05 and 1 other fieldsHigh correlation
PayStat/Jul05 is highly correlated with PayStat/Aug05 and 3 other fieldsHigh correlation
PayStat/Jun05 is highly correlated with PayStat/Jul05 and 2 other fieldsHigh correlation
PayStat/May05 is highly correlated with PayStat/Jul05 and 2 other fieldsHigh correlation
PayStat/Apr05 is highly correlated with PayStat/Jul05 and 2 other fieldsHigh correlation
Outstanding/Sept05 is highly correlated with Credit Limit and 7 other fieldsHigh correlation
Outstanding/Aug05 is highly correlated with Credit Limit and 8 other fieldsHigh correlation
Outstanding/Jul05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Outstanding/Jun05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Outstanding/May05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Outstanding/Apr05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Paid/Sept05 is highly correlated with Outstanding/Aug05 and 5 other fieldsHigh correlation
Paid/Aug05 is highly correlated with Outstanding/Jul05 and 3 other fieldsHigh correlation
Paid/Jul05 is highly correlated with Outstanding/Sept05 and 6 other fieldsHigh correlation
Paid/Jun05 is highly correlated with Outstanding/Sept05 and 5 other fieldsHigh correlation
Credit Limit is highly correlated with Outstanding/Sept05 and 5 other fieldsHigh correlation
PayStat/Jul05 is highly correlated with PayStat/Jun05High correlation
PayStat/Jun05 is highly correlated with PayStat/Jul05 and 2 other fieldsHigh correlation
PayStat/May05 is highly correlated with PayStat/Jun05 and 1 other fieldsHigh correlation
PayStat/Apr05 is highly correlated with PayStat/Jun05 and 1 other fieldsHigh correlation
Outstanding/Sept05 is highly correlated with Credit Limit and 10 other fieldsHigh correlation
Outstanding/Aug05 is highly correlated with Credit Limit and 10 other fieldsHigh correlation
Outstanding/Jul05 is highly correlated with Credit Limit and 11 other fieldsHigh correlation
Outstanding/Jun05 is highly correlated with Credit Limit and 11 other fieldsHigh correlation
Outstanding/May05 is highly correlated with Credit Limit and 11 other fieldsHigh correlation
Outstanding/Apr05 is highly correlated with Credit Limit and 11 other fieldsHigh correlation
Paid/Sept05 is highly correlated with Outstanding/Sept05 and 6 other fieldsHigh correlation
Paid/Aug05 is highly correlated with Outstanding/Sept05 and 6 other fieldsHigh correlation
Paid/Jul05 is highly correlated with Outstanding/Sept05 and 6 other fieldsHigh correlation
Paid/Jun05 is highly correlated with Outstanding/Sept05 and 6 other fieldsHigh correlation
Paid/May05 is highly correlated with Outstanding/Sept05 and 5 other fieldsHigh correlation
Paid/Apr05 is highly correlated with Outstanding/Jul05 and 3 other fieldsHigh correlation
Unnamed: 0 is highly correlated with PayStat/Aug05 and 5 other fieldsHigh correlation
Credit Limit is highly correlated with PayStat/Aug05 and 11 other fieldsHigh correlation
Age is highly correlated with PayStat/Aug05 and 5 other fieldsHigh correlation
PayStat/Sept05 is highly correlated with PayStat/Aug05 and 4 other fieldsHigh correlation
PayStat/Aug05 is highly correlated with Unnamed: 0 and 8 other fieldsHigh correlation
PayStat/Jul05 is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
PayStat/Jun05 is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
PayStat/May05 is highly correlated with Unnamed: 0 and 5 other fieldsHigh correlation
PayStat/Apr05 is highly correlated with Unnamed: 0 and 6 other fieldsHigh correlation
Outstanding/Sept05 is highly correlated with Credit Limit and 6 other fieldsHigh correlation
Outstanding/Aug05 is highly correlated with Credit Limit and 6 other fieldsHigh correlation
Outstanding/Jul05 is highly correlated with Credit Limit and 6 other fieldsHigh correlation
Outstanding/Jun05 is highly correlated with Credit Limit and 6 other fieldsHigh correlation
Outstanding/May05 is highly correlated with Credit Limit and 6 other fieldsHigh correlation
Outstanding/Apr05 is highly correlated with Credit Limit and 6 other fieldsHigh correlation
Paid/Sept05 is highly correlated with DefaultHigh correlation
Paid/Aug05 is highly correlated with DefaultHigh correlation
Paid/Jul05 is highly correlated with DefaultHigh correlation
Paid/Jun05 is highly correlated with DefaultHigh correlation
Paid/May05 is highly correlated with DefaultHigh correlation
Paid/Apr05 is highly correlated with DefaultHigh correlation
Default is highly correlated with Unnamed: 0 and 19 other fieldsHigh correlation
Marital Status is highly correlated with AgeHigh correlation
Credit Limit is highly correlated with Outstanding/Aug05 and 8 other fieldsHigh correlation
Outstanding/Aug05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Paid/May05 is highly correlated with Paid/Sept05 and 1 other fieldsHigh correlation
PayStat/Jun05 is highly correlated with PayStat/May05 and 5 other fieldsHigh correlation
PayStat/May05 is highly correlated with PayStat/Jun05 and 2 other fieldsHigh correlation
Outstanding/Jun05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Outstanding/May05 is highly correlated with Credit Limit and 10 other fieldsHigh correlation
Paid/Sept05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Paid/Aug05 is highly correlated with Outstanding/Jun05 and 2 other fieldsHigh correlation
PayStat/Jul05 is highly correlated with PayStat/Jun05 and 5 other fieldsHigh correlation
Paid/Jun05 is highly correlated with Credit Limit and 8 other fieldsHigh correlation
PayStat/Apr05 is highly correlated with PayStat/Jun05 and 2 other fieldsHigh correlation
Age is highly correlated with Marital StatusHigh correlation
PayStat/Aug05 is highly correlated with PayStat/Jun05 and 2 other fieldsHigh correlation
PayStat/Sept05 is highly correlated with PayStat/Jun05 and 2 other fieldsHigh correlation
Outstanding/Sept05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Outstanding/Apr05 is highly correlated with Credit Limit and 9 other fieldsHigh correlation
Outstanding/Jul05 is highly correlated with Credit Limit and 10 other fieldsHigh correlation
Paid/Jul05 is highly correlated with Credit Limit and 8 other fieldsHigh correlation
Paid/Apr05 is highly correlated with Outstanding/Aug05 and 5 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Paid/Sept05 has 501 (37.6%) zeros Zeros
Paid/Aug05 has 470 (35.2%) zeros Zeros
Paid/Jul05 has 523 (39.2%) zeros Zeros
Paid/Jun05 has 494 (37.0%) zeros Zeros
Paid/May05 has 586 (43.9%) zeros Zeros
Paid/Apr05 has 531 (39.8%) zeros Zeros

Reproduction

Analysis started2021-07-29 23:16:33.405875
Analysis finished2021-07-29 23:17:41.086745
Duration1 minute and 7.68 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1334
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean666.5
Minimum0
Maximum1333
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:41.212435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66.65
Q1333.25
median666.5
Q3999.75
95-th percentile1266.35
Maximum1333
Range1333
Interquartile range (IQR)666.5

Descriptive statistics

Standard deviation385.2369401
Coefficient of variation (CV)0.5779999101
Kurtosis-1.2
Mean666.5
Median Absolute Deviation (MAD)333.5
Skewness0
Sum889111
Variance148407.5
MonotonicityStrictly increasing
2021-07-29T17:17:41.369008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
8961
 
0.1%
8941
 
0.1%
8931
 
0.1%
8921
 
0.1%
8911
 
0.1%
8901
 
0.1%
8891
 
0.1%
8881
 
0.1%
8871
 
0.1%
Other values (1324)1324
99.3%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
13331
0.1%
13321
0.1%
13311
0.1%
13301
0.1%
13291
0.1%
13281
0.1%
13271
0.1%
13261
0.1%
13251
0.1%
13241
0.1%

Credit Limit
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct43
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94122.93853
Minimum10000
Maximum580000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:41.521576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q130000
median70000
Q3130000
95-th percentile263500
Maximum580000
Range570000
Interquartile range (IQR)100000

Descriptive statistics

Standard deviation84817.98985
Coefficient of variation (CV)0.9011404783
Kurtosis4.248451848
Mean94122.93853
Median Absolute Deviation (MAD)40000
Skewness1.840858905
Sum125560000
Variance7194091401
MonotonicityNot monotonic
2021-07-29T17:17:41.655210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
30000194
14.5%
50000179
13.4%
20000161
12.1%
10000085
 
6.4%
8000072
 
5.4%
7000067
 
5.0%
6000051
 
3.8%
20000049
 
3.7%
1000047
 
3.5%
12000041
 
3.1%
Other values (33)388
29.1%
ValueCountFrequency (%)
1000047
 
3.5%
20000161
12.1%
30000194
14.5%
4000028
 
2.1%
50000179
13.4%
6000051
 
3.8%
7000067
 
5.0%
8000072
 
5.4%
9000034
 
2.5%
10000085
6.4%
ValueCountFrequency (%)
5800001
 
0.1%
5100001
 
0.1%
5000004
0.3%
4600003
0.2%
4300002
 
0.1%
4000005
0.4%
3900001
 
0.1%
3800004
0.3%
3600004
0.3%
3500004
0.3%

Sex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
F
708 
M
626 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1334
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F708
53.1%
M626
46.9%

Length

2021-07-29T17:17:41.887633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-29T17:17:41.958399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
f708
53.1%
m626
46.9%

Most occurring characters

ValueCountFrequency (%)
F708
53.1%
M626
46.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1334
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F708
53.1%
M626
46.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1334
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F708
53.1%
M626
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F708
53.1%
M626
46.9%

Education
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
BSc
736 
MSc or PHd
336 
High School Diploma
262 

Length

Max length19
Median length3
Mean length7.905547226
Min length3

Characters and Unicode

Total characters10546
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMSc or PHd
2nd rowBSc
3rd rowMSc or PHd
4th rowBSc
5th rowHigh School Diploma

Common Values

ValueCountFrequency (%)
BSc736
55.2%
MSc or PHd336
25.2%
High School Diploma262
 
19.6%

Length

2021-07-29T17:17:42.160858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-29T17:17:42.246631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
bsc736
29.1%
msc336
13.3%
or336
13.3%
phd336
13.3%
high262
 
10.4%
school262
 
10.4%
diploma262
 
10.4%

Most occurring characters

ValueCountFrequency (%)
S1334
12.6%
c1334
12.6%
1196
11.3%
o1122
10.6%
B736
 
7.0%
H598
 
5.7%
i524
 
5.0%
h524
 
5.0%
l524
 
5.0%
M336
 
3.2%
Other values (8)2318
22.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5748
54.5%
Uppercase Letter3602
34.2%
Space Separator1196
 
11.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c1334
23.2%
o1122
19.5%
i524
 
9.1%
h524
 
9.1%
l524
 
9.1%
r336
 
5.8%
d336
 
5.8%
g262
 
4.6%
p262
 
4.6%
m262
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
S1334
37.0%
B736
20.4%
H598
16.6%
M336
 
9.3%
P336
 
9.3%
D262
 
7.3%
Space Separator
ValueCountFrequency (%)
1196
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9350
88.7%
Common1196
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1334
14.3%
c1334
14.3%
o1122
12.0%
B736
 
7.9%
H598
 
6.4%
i524
 
5.6%
h524
 
5.6%
l524
 
5.6%
M336
 
3.6%
r336
 
3.6%
Other values (7)1982
21.2%
Common
ValueCountFrequency (%)
1196
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII10546
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1334
12.6%
c1334
12.6%
1196
11.3%
o1122
10.6%
B736
 
7.0%
H598
 
5.7%
i524
 
5.0%
h524
 
5.0%
l524
 
5.0%
M336
 
3.2%
Other values (8)2318
22.0%

Marital Status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
Single
675 
Married
643 
Other
 
16

Length

Max length7
Median length6
Mean length6.470014993
Min length5

Characters and Unicode

Total characters8631
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowMarried
4th rowMarried
5th rowSingle

Common Values

ValueCountFrequency (%)
Single675
50.6%
Married643
48.2%
Other16
 
1.2%

Length

2021-07-29T17:17:42.476568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-07-29T17:17:42.563804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
single675
50.6%
married643
48.2%
other16
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e1334
15.5%
i1318
15.3%
r1302
15.1%
S675
7.8%
n675
7.8%
g675
7.8%
l675
7.8%
M643
7.4%
a643
7.4%
d643
7.4%
Other values (3)48
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7297
84.5%
Uppercase Letter1334
 
15.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1334
18.3%
i1318
18.1%
r1302
17.8%
n675
9.3%
g675
9.3%
l675
9.3%
a643
8.8%
d643
8.8%
t16
 
0.2%
h16
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
S675
50.6%
M643
48.2%
O16
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin8631
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1334
15.5%
i1318
15.3%
r1302
15.1%
S675
7.8%
n675
7.8%
g675
7.8%
l675
7.8%
M643
7.4%
a643
7.4%
d643
7.4%
Other values (3)48
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII8631
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e1334
15.5%
i1318
15.3%
r1302
15.1%
S675
7.8%
n675
7.8%
g675
7.8%
l675
7.8%
M643
7.4%
a643
7.4%
d643
7.4%
Other values (3)48
 
0.6%

Age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.66941529
Minimum22
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:42.670568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile23
Q127
median34
Q342.75
95-th percentile55
Maximum67
Range45
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation9.876097075
Coefficient of variation (CV)0.2768785805
Kurtosis-0.3780144742
Mean35.66941529
Median Absolute Deviation (MAD)7
Skewness0.6587798239
Sum47583
Variance97.53729343
MonotonicityNot monotonic
2021-07-29T17:17:42.834158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2972
 
5.4%
2470
 
5.2%
2670
 
5.2%
3059
 
4.4%
2559
 
4.4%
3656
 
4.2%
2356
 
4.2%
2755
 
4.1%
2852
 
3.9%
3450
 
3.7%
Other values (36)735
55.1%
ValueCountFrequency (%)
2229
2.2%
2356
4.2%
2470
5.2%
2559
4.4%
2670
5.2%
2755
4.1%
2852
3.9%
2972
5.4%
3059
4.4%
3145
3.4%
ValueCountFrequency (%)
672
 
0.1%
661
 
0.1%
651
 
0.1%
641
 
0.1%
631
 
0.1%
623
 
0.2%
612
 
0.1%
604
 
0.3%
5910
0.7%
5813
1.0%

PayStat/Sept05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.008245877
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:42.946091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.133579826
Coefficient of variation (CV)0.5644626682
Kurtosis11.85229906
Mean2.008245877
Median Absolute Deviation (MAD)0
Skewness2.892821297
Sum2679
Variance1.285003222
MonotonicityNot monotonic
2021-07-29T17:17:43.042133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2710
53.2%
1401
30.1%
3151
 
11.3%
433
 
2.5%
819
 
1.4%
79
 
0.7%
58
 
0.6%
63
 
0.2%
ValueCountFrequency (%)
1401
30.1%
2710
53.2%
3151
 
11.3%
433
 
2.5%
58
 
0.6%
63
 
0.2%
79
 
0.7%
819
 
1.4%
ValueCountFrequency (%)
819
 
1.4%
79
 
0.7%
63
 
0.2%
58
 
0.6%
433
 
2.5%
3151
 
11.3%
2710
53.2%
1401
30.1%

PayStat/Aug05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.29910045
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:43.154355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8720288407
Coefficient of variation (CV)0.3792913184
Kurtosis15.22404579
Mean2.29910045
Median Absolute Deviation (MAD)0
Skewness3.731807335
Sum3067
Variance0.760434299
MonotonicityNot monotonic
2021-07-29T17:17:43.259026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
21119
83.9%
3118
 
8.8%
453
 
4.0%
720
 
1.5%
612
 
0.9%
58
 
0.6%
13
 
0.2%
81
 
0.1%
ValueCountFrequency (%)
13
 
0.2%
21119
83.9%
3118
 
8.8%
453
 
4.0%
58
 
0.6%
612
 
0.9%
720
 
1.5%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
720
 
1.5%
612
 
0.9%
58
 
0.6%
453
 
4.0%
3118
 
8.8%
21119
83.9%
13
 
0.2%

PayStat/Jul05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.37856072
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:43.360209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.037853271
Coefficient of variation (CV)0.4363366731
Kurtosis10.35964989
Mean2.37856072
Median Absolute Deviation (MAD)0
Skewness3.235328932
Sum3173
Variance1.077139412
MonotonicityNot monotonic
2021-07-29T17:17:43.462555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
21106
82.9%
3107
 
8.0%
447
 
3.5%
727
 
2.0%
623
 
1.7%
520
 
1.5%
83
 
0.2%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
21106
82.9%
3107
 
8.0%
447
 
3.5%
520
 
1.5%
623
 
1.7%
727
 
2.0%
83
 
0.2%
ValueCountFrequency (%)
83
 
0.2%
727
 
2.0%
623
 
1.7%
520
 
1.5%
447
 
3.5%
3107
 
8.0%
21106
82.9%
11
 
0.1%

PayStat/Jun05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.47976012
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:43.563290image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q32
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.202321312
Coefficient of variation (CV)0.4848538784
Kurtosis7.251660708
Mean2.47976012
Median Absolute Deviation (MAD)0
Skewness2.816384227
Sum3308
Variance1.445576537
MonotonicityNot monotonic
2021-07-29T17:17:43.662026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
21074
80.5%
3101
 
7.6%
460
 
4.5%
758
 
4.3%
534
 
2.5%
64
 
0.3%
82
 
0.1%
11
 
0.1%
ValueCountFrequency (%)
11
 
0.1%
21074
80.5%
3101
 
7.6%
460
 
4.5%
534
 
2.5%
64
 
0.3%
758
 
4.3%
82
 
0.1%
ValueCountFrequency (%)
82
 
0.1%
758
 
4.3%
64
 
0.3%
534
 
2.5%
460
 
4.5%
3101
 
7.6%
21074
80.5%
11
 
0.1%

PayStat/May05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.464767616
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:43.773693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile5
Maximum8
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.156164883
Coefficient of variation (CV)0.4690766285
Kurtosis8.265007048
Mean2.464767616
Median Absolute Deviation (MAD)0
Skewness2.951674377
Sum3288
Variance1.336717238
MonotonicityNot monotonic
2021-07-29T17:17:43.866436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21061
79.5%
3123
 
9.2%
474
 
5.5%
757
 
4.3%
514
 
1.0%
64
 
0.3%
81
 
0.1%
ValueCountFrequency (%)
21061
79.5%
3123
 
9.2%
474
 
5.5%
514
 
1.0%
64
 
0.3%
757
 
4.3%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
757
 
4.3%
64
 
0.3%
514
 
1.0%
474
 
5.5%
3123
 
9.2%
21061
79.5%

PayStat/Apr05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.392053973
Minimum2
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:43.967203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2
Q32
95-th percentile5
Maximum8
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.096251463
Coefficient of variation (CV)0.4582887659
Kurtosis10.46639397
Mean2.392053973
Median Absolute Deviation (MAD)0
Skewness3.307902885
Sum3191
Variance1.201767271
MonotonicityNot monotonic
2021-07-29T17:17:44.065946image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21106
82.9%
3116
 
8.7%
746
 
3.4%
441
 
3.1%
614
 
1.0%
59
 
0.7%
82
 
0.1%
ValueCountFrequency (%)
21106
82.9%
3116
 
8.7%
441
 
3.1%
59
 
0.7%
614
 
1.0%
746
 
3.4%
82
 
0.1%
ValueCountFrequency (%)
82
 
0.1%
746
 
3.4%
614
 
1.0%
59
 
0.7%
441
 
3.1%
3116
 
8.7%
21106
82.9%

Outstanding/Sept05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1216
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53442.52699
Minimum37
Maximum477094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:44.194560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile1650
Q115314
median31174
Q369581
95-th percentile175382.9
Maximum477094
Range477057
Interquartile range (IQR)54267

Descriptive statistics

Standard deviation61159.29248
Coefficient of variation (CV)1.144393724
Kurtosis7.986166897
Mean53442.52699
Median Absolute Deviation (MAD)21838
Skewness2.431697049
Sum71292331
Variance3740459057
MonotonicityNot monotonic
2021-07-29T17:17:44.350744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240037
 
2.8%
105020
 
1.5%
250016
 
1.2%
16509
 
0.7%
3009
 
0.7%
6007
 
0.5%
1505
 
0.4%
12004
 
0.3%
50003
 
0.2%
12503
 
0.2%
Other values (1206)1221
91.5%
ValueCountFrequency (%)
371
 
0.1%
1421
 
0.1%
1505
0.4%
2001
 
0.1%
2501
 
0.1%
3009
0.7%
3221
 
0.1%
4001
 
0.1%
4201
 
0.1%
4502
 
0.1%
ValueCountFrequency (%)
4770941
0.1%
4539851
0.1%
4053661
0.1%
3777791
0.1%
3639441
0.1%
3520161
0.1%
3392021
0.1%
3272891
0.1%
3221991
0.1%
3159121
0.1%

Outstanding/Aug05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1211
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54296.37706
Minimum37
Maximum470915
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:44.499338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile1650
Q116043.5
median32226.5
Q370999.5
95-th percentile176075.25
Maximum470915
Range470878
Interquartile range (IQR)54956

Descriptive statistics

Standard deviation61503.95506
Coefficient of variation (CV)1.1327451
Kurtosis7.929281164
Mean54296.37706
Median Absolute Deviation (MAD)21716.5
Skewness2.417775595
Sum72431367
Variance3782736487
MonotonicityNot monotonic
2021-07-29T17:17:44.654914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240038
 
2.8%
105020
 
1.5%
250017
 
1.3%
16509
 
0.7%
3009
 
0.7%
6007
 
0.5%
1505
 
0.4%
12004
 
0.3%
12503
 
0.2%
50003
 
0.2%
Other values (1201)1219
91.4%
ValueCountFrequency (%)
371
 
0.1%
1421
 
0.1%
1505
0.4%
2001
 
0.1%
2501
 
0.1%
3009
0.7%
3221
 
0.1%
4001
 
0.1%
4201
 
0.1%
4502
 
0.1%
ValueCountFrequency (%)
4709151
0.1%
4698821
0.1%
3977541
0.1%
3857261
0.1%
3723551
0.1%
3416001
0.1%
3374951
0.1%
3315181
0.1%
3231251
0.1%
3221991
0.1%

Outstanding/Jul05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1211
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55103.47151
Minimum37
Maximum471175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:44.796969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile1650
Q116773.5
median32965
Q372129.25
95-th percentile179931.1
Maximum471175
Range471138
Interquartile range (IQR)55355.75

Descriptive statistics

Standard deviation61922.17373
Coefficient of variation (CV)1.123743605
Kurtosis7.682939156
Mean55103.47151
Median Absolute Deviation (MAD)22079.5
Skewness2.396294537
Sum73508031
Variance3834355599
MonotonicityNot monotonic
2021-07-29T17:17:44.946527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240038
 
2.8%
105020
 
1.5%
250017
 
1.3%
16509
 
0.7%
3009
 
0.7%
6007
 
0.5%
1505
 
0.4%
12004
 
0.3%
50003
 
0.2%
12503
 
0.2%
Other values (1201)1219
91.4%
ValueCountFrequency (%)
371
 
0.1%
1421
 
0.1%
1505
0.4%
2001
 
0.1%
2501
 
0.1%
3009
0.7%
3221
 
0.1%
4001
 
0.1%
4201
 
0.1%
4502
 
0.1%
ValueCountFrequency (%)
4711751
0.1%
4614021
0.1%
3899031
0.1%
3830301
0.1%
3683181
0.1%
3652501
0.1%
3377031
0.1%
3315181
0.1%
3271931
0.1%
3257001
0.1%

Outstanding/Jun05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1218
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55413.89505
Minimum37
Maximum486776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:45.091181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile1650
Q117107
median32529.5
Q372461
95-th percentile180522.7
Maximum486776
Range486739
Interquartile range (IQR)55354

Descriptive statistics

Standard deviation62304.97344
Coefficient of variation (CV)1.124356506
Kurtosis7.763338922
Mean55413.89505
Median Absolute Deviation (MAD)21929
Skewness2.413919106
Sum73922136
Variance3881909715
MonotonicityNot monotonic
2021-07-29T17:17:45.253703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240038
 
2.8%
105020
 
1.5%
250017
 
1.3%
16509
 
0.7%
3008
 
0.6%
6007
 
0.5%
1504
 
0.3%
12004
 
0.3%
50003
 
0.2%
12503
 
0.2%
Other values (1208)1221
91.5%
ValueCountFrequency (%)
371
 
0.1%
1421
 
0.1%
1504
0.3%
2001
 
0.1%
2501
 
0.1%
3008
0.6%
3221
 
0.1%
4001
 
0.1%
4201
 
0.1%
4502
 
0.1%
ValueCountFrequency (%)
4867761
0.1%
4524051
0.1%
3849811
0.1%
3771451
0.1%
3581451
0.1%
3544431
0.1%
3478131
0.1%
3436121
0.1%
3377031
0.1%
3332781
0.1%

Outstanding/May05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1222
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55841.19415
Minimum37
Maximum503914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:45.405158image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile1650
Q117696.75
median33886.5
Q373725.25
95-th percentile181247.95
Maximum503914
Range503877
Interquartile range (IQR)56028.5

Descriptive statistics

Standard deviation62408.0328
Coefficient of variation (CV)1.117598464
Kurtosis7.69860006
Mean55841.19415
Median Absolute Deviation (MAD)21733.5
Skewness2.393487165
Sum74492153
Variance3894762558
MonotonicityNot monotonic
2021-07-29T17:17:45.560186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240037
 
2.8%
105020
 
1.5%
250017
 
1.3%
30010
 
0.7%
16509
 
0.7%
6006
 
0.4%
1504
 
0.3%
12003
 
0.2%
50003
 
0.2%
995782
 
0.1%
Other values (1212)1223
91.7%
ValueCountFrequency (%)
371
 
0.1%
1421
 
0.1%
1504
 
0.3%
2001
 
0.1%
2501
 
0.1%
30010
0.7%
3221
 
0.1%
4001
 
0.1%
4201
 
0.1%
4501
 
0.1%
ValueCountFrequency (%)
5039141
0.1%
4436571
0.1%
3818631
0.1%
3708501
0.1%
3547651
0.1%
3521211
0.1%
3510891
0.1%
3436121
0.1%
3407911
0.1%
3332781
0.1%

Outstanding/Apr05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1214
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55778.20465
Minimum0
Maximum527711
Zeros8
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:45.715820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1050
Q117297.25
median33225
Q374713.5
95-th percentile182877.65
Maximum527711
Range527711
Interquartile range (IQR)57416.25

Descriptive statistics

Standard deviation62675.61222
Coefficient of variation (CV)1.123657755
Kurtosis7.685331698
Mean55778.20465
Median Absolute Deviation (MAD)22350.5
Skewness2.368803436
Sum74408125
Variance3928232367
MonotonicityNot monotonic
2021-07-29T17:17:45.868385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240030
 
2.2%
105020
 
1.5%
250016
 
1.2%
30012
 
0.9%
16509
 
0.7%
08
 
0.6%
18007
 
0.5%
1507
 
0.5%
6004
 
0.3%
50003
 
0.2%
Other values (1204)1218
91.3%
ValueCountFrequency (%)
08
0.6%
371
 
0.1%
1421
 
0.1%
1507
0.5%
2001
 
0.1%
2501
 
0.1%
30012
0.9%
3221
 
0.1%
3501
 
0.1%
4001
 
0.1%
ValueCountFrequency (%)
5277111
0.1%
4373051
0.1%
3643651
0.1%
3593141
0.1%
3584011
0.1%
3545791
0.1%
3510891
0.1%
3480061
0.1%
3462161
0.1%
3407911
0.1%

Paid/Sept05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct216
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2456.195652
Minimum0
Maximum59872
Zeros501
Zeros (%)37.6%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:46.028584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1700
Q33300
95-th percentile8200
Maximum59872
Range59872
Interquartile range (IQR)3300

Descriptive statistics

Standard deviation3811.551544
Coefficient of variation (CV)1.551811046
Kurtosis71.26477174
Mean2456.195652
Median Absolute Deviation (MAD)1700
Skewness6.055610761
Sum3276565
Variance14527925.17
MonotonicityNot monotonic
2021-07-29T17:17:46.172241image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0501
37.6%
200098
 
7.3%
300053
 
4.0%
150041
 
3.1%
100027
 
2.0%
180026
 
1.9%
500023
 
1.7%
250021
 
1.6%
160021
 
1.6%
350019
 
1.4%
Other values (206)504
37.8%
ValueCountFrequency (%)
0501
37.6%
31
 
0.1%
101
 
0.1%
171
 
0.1%
501
 
0.1%
1501
 
0.1%
2002
 
0.1%
2061
 
0.1%
3002
 
0.1%
3661
 
0.1%
ValueCountFrequency (%)
598721
0.1%
558871
0.1%
295401
0.1%
257041
0.1%
225001
0.1%
200001
0.1%
183001
0.1%
179441
0.1%
160011
0.1%
152101
0.1%

Paid/Aug05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct248
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2526.22039
Minimum0
Maximum97225
Zeros470
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:46.327788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1600
Q33300
95-th percentile8117.5
Maximum97225
Range97225
Interquartile range (IQR)3300

Descriptive statistics

Standard deviation4491.354954
Coefficient of variation (CV)1.77789514
Kurtosis168.8490359
Mean2526.22039
Median Absolute Deviation (MAD)1600
Skewness9.704326315
Sum3369978
Variance20172269.32
MonotonicityNot monotonic
2021-07-29T17:17:46.489868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0470
35.2%
200077
 
5.8%
300055
 
4.1%
100038
 
2.8%
150036
 
2.7%
500031
 
2.3%
250024
 
1.8%
400024
 
1.8%
600020
 
1.5%
160018
 
1.3%
Other values (238)541
40.6%
ValueCountFrequency (%)
0470
35.2%
41
 
0.1%
71
 
0.1%
81
 
0.1%
111
 
0.1%
141
 
0.1%
161
 
0.1%
301
 
0.1%
501
 
0.1%
601
 
0.1%
ValueCountFrequency (%)
972251
0.1%
561001
0.1%
420001
0.1%
283411
0.1%
222001
0.1%
220001
0.1%
195001
0.1%
183001
0.1%
181121
0.1%
180002
0.1%

Paid/Jul05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct223
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2149.708396
Minimum0
Maximum30334
Zeros523
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:46.647998image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1200
Q33000
95-th percentile8000
Maximum30334
Range30334
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation3175.846662
Coefficient of variation (CV)1.477338354
Kurtosis14.74567162
Mean2149.708396
Median Absolute Deviation (MAD)1200
Skewness3.093446261
Sum2867711
Variance10086002.02
MonotonicityNot monotonic
2021-07-29T17:17:46.790620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0523
39.2%
200069
 
5.2%
100058
 
4.3%
300043
 
3.2%
150029
 
2.2%
250024
 
1.8%
500019
 
1.4%
350017
 
1.3%
120016
 
1.2%
400016
 
1.2%
Other values (213)520
39.0%
ValueCountFrequency (%)
0523
39.2%
22
 
0.1%
41
 
0.1%
51
 
0.1%
122
 
0.1%
181
 
0.1%
571
 
0.1%
701
 
0.1%
1501
 
0.1%
2002
 
0.1%
ValueCountFrequency (%)
303341
 
0.1%
250651
 
0.1%
250001
 
0.1%
240001
 
0.1%
220531
 
0.1%
207831
 
0.1%
200001
 
0.1%
180001
 
0.1%
154051
 
0.1%
150004
0.3%

Paid/Jun05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct259
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2254.522489
Minimum0
Maximum61923
Zeros494
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:46.946164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1300
Q33000
95-th percentile8000
Maximum61923
Range61923
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation3796.414068
Coefficient of variation (CV)1.68391049
Kurtosis63.91624863
Mean2254.522489
Median Absolute Deviation (MAD)1300
Skewness5.868390286
Sum3007533
Variance14412759.78
MonotonicityNot monotonic
2021-07-29T17:17:47.089816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0494
37.0%
200058
 
4.3%
100046
 
3.4%
150044
 
3.3%
300035
 
2.6%
500021
 
1.6%
250020
 
1.5%
400018
 
1.3%
110017
 
1.3%
120016
 
1.2%
Other values (249)565
42.4%
ValueCountFrequency (%)
0494
37.0%
11
 
0.1%
31
 
0.1%
41
 
0.1%
51
 
0.1%
111
 
0.1%
131
 
0.1%
391
 
0.1%
1002
 
0.1%
1061
 
0.1%
ValueCountFrequency (%)
619231
0.1%
402001
0.1%
400001
0.1%
257031
0.1%
238001
0.1%
237001
0.1%
200001
0.1%
191721
0.1%
190001
0.1%
170001
0.1%

Paid/May05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct249
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2054.632684
Minimum0
Maximum100000
Zeros586
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:47.248395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1000
Q32500
95-th percentile7556.35
Maximum100000
Range100000
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation4438.258009
Coefficient of variation (CV)2.160122364
Kurtosis195.4698394
Mean2054.632684
Median Absolute Deviation (MAD)1000
Skewness10.69579406
Sum2740880
Variance19698134.15
MonotonicityNot monotonic
2021-07-29T17:17:47.398440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0586
43.9%
200060
 
4.5%
100039
 
2.9%
300036
 
2.7%
150035
 
2.6%
400021
 
1.6%
500020
 
1.5%
250018
 
1.3%
180016
 
1.2%
120015
 
1.1%
Other values (239)488
36.6%
ValueCountFrequency (%)
0586
43.9%
11
 
0.1%
21
 
0.1%
52
 
0.1%
61
 
0.1%
71
 
0.1%
91
 
0.1%
111
 
0.1%
201
 
0.1%
221
 
0.1%
ValueCountFrequency (%)
1000001
0.1%
500001
0.1%
350001
0.1%
330001
0.1%
322361
0.1%
320001
0.1%
271501
0.1%
200001
0.1%
190001
0.1%
181001
0.1%

Paid/Apr05
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct291
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2436.943778
Minimum0
Maximum254000
Zeros531
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size10.5 KiB
2021-07-29T17:17:47.544943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1000
Q32500
95-th percentile7000
Maximum254000
Range254000
Interquartile range (IQR)2500

Descriptive statistics

Standard deviation9964.599582
Coefficient of variation (CV)4.088973932
Kurtosis355.2888351
Mean2436.943778
Median Absolute Deviation (MAD)1000
Skewness16.81353102
Sum3250883
Variance99293244.83
MonotonicityNot monotonic
2021-07-29T17:17:47.702521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0531
39.8%
200066
 
4.9%
100048
 
3.6%
300043
 
3.2%
150041
 
3.1%
250020
 
1.5%
160018
 
1.3%
500017
 
1.3%
120016
 
1.2%
400015
 
1.1%
Other values (281)519
38.9%
ValueCountFrequency (%)
0531
39.8%
15
 
0.4%
21
 
0.1%
31
 
0.1%
52
 
0.1%
61
 
0.1%
71
 
0.1%
81
 
0.1%
101
 
0.1%
141
 
0.1%
ValueCountFrequency (%)
2540001
0.1%
1266851
0.1%
1200211
0.1%
1080581
0.1%
1053001
0.1%
572581
0.1%
426171
0.1%
409251
0.1%
320001
0.1%
314961
0.1%

Default
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
True
942 
False
392 
ValueCountFrequency (%)
True942
70.6%
False392
29.4%
2021-07-29T17:17:47.801693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2021-07-29T17:16:38.272459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:38.429110image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:38.558360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:38.677105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:38.807719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:38.935961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:39.065615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:39.192308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:39.318938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:39.445598image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:39.573258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:39.705941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:39.828618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:40.050495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:40.180154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:40.310844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:40.442356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:40.570362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:40.698089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:40.823929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:40.950096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:41.078151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:41.216012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:41.346438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:41.466161image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:41.601791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:41.732449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:41.866091image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:41.994704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:42.134381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:42.268622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:42.410369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:42.541960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:42.677134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:42.816275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:42.944934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:43.083548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:43.217144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:43.348751image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:43.481435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:43.614040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:43.856478image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:43.987130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:44.108763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:44.230984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:44.346677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:44.467352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:44.592016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:44.711699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:44.829379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:44.954051image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:45.072731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:45.194367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:45.321028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:45.439757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:45.564116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:45.692208image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:45.810367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:45.931040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:46.047769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:46.177419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:46.292073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:46.407765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:46.527488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:46.659131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:46.802794image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:46.924551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:47.055162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:47.191513image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:47.326711image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:47.459164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:47.585984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:47.728642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:47.856260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:47.985954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:48.110615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:48.365938image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:48.496588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:48.633533image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:48.765153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:48.892813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:49.026006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:49.154181image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:49.290772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:49.425549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:49.554052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:49.685698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:49.806415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:49.939019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:50.067722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:50.196373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:50.325597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:50.460339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:50.588054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:50.718661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:50.848162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:50.976026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:51.114167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:51.252852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:51.391083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:51.522772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:51.655377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:51.795002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:51.934672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:52.081279image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:52.218472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:52.362134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:52.508743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:52.645339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:52.787510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:52.958015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:53.103627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:53.254225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:53.397475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:53.534105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:53.671908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:16:53.801572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
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2021-07-29T17:17:23.819645image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:23.962265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:24.098912image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:24.243157image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:24.383301image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:24.518812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:24.643496image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:24.780127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:24.911800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:25.074383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:25.231022image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:25.365227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:25.539594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:25.695006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:25.835622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:26.005170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:26.166122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:26.295390image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:26.425044image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:26.545902image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:26.679545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:26.790286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:26.912955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:27.043571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:27.182201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:27.308897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:27.427572image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:27.551243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:27.686937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:27.803585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:27.923785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:28.047481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:28.171125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:28.290803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:28.416508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:28.540176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:28.661855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:28.787487image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:28.907778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:29.025468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:29.152167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:29.292831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:29.411397image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:29.541012image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:29.668974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:29.804923image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:29.928843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:30.059035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:30.184980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:30.314116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:30.439740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:30.560419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:30.694079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:30.825710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:30.958354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:31.086050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:31.212710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:31.350374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:31.479273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:31.609021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:31.736153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:31.860905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:31.993028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:32.113704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:32.255326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:32.383029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:32.512678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:32.640296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:32.767995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:32.897698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:33.028307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:33.167984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:33.299722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:33.433559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:33.644313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:33.795230image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:33.928874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:34.060521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:34.186186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:34.316880image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:34.449520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:34.583124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:34.715350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:34.846504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:34.960252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:35.085225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:35.209934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:35.342398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:35.470838image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:35.594060image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:35.719727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:35.838406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:35.959045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:36.078725image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:36.206396image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:36.329094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:36.450652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:36.926005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:37.047101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:37.179778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:37.304452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:37.424133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:37.549757image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:37.677464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:37.806111image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:37.917814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:38.041514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:38.169132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:38.291670image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:38.420379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:38.537704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:38.660370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:38.787030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:38.912656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:39.036365image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:39.157045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:39.287693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:39.413876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:39.537588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:39.664275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:39.791947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:39.917356image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-07-29T17:17:40.043970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-07-29T17:17:47.923368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-07-29T17:17:48.249058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-07-29T17:17:48.599953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-07-29T17:17:48.974948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-07-29T17:17:49.317061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-07-29T17:17:40.344202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-07-29T17:17:40.905694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0Credit LimitSexEducationMarital StatusAgePayStat/Sept05PayStat/Aug05PayStat/Jul05PayStat/Jun05PayStat/May05PayStat/Apr05Outstanding/Sept05Outstanding/Aug05Outstanding/Jul05Outstanding/Jun05Outstanding/May05Outstanding/Apr05Paid/Sept05Paid/Aug05Paid/Jul05Paid/Jun05Paid/May05Paid/Apr05Default
0050000MMSc or PHdSingle29222222249872430026591258652766728264027000222512000True
11320000MBScSingle29222222582675924660184586226230763526250025000480024001600True
22200000MMSc or PHdMarried53222222138180140774142460144098147124149531630055005500550050005000True
33280000MBScMarried4122222313567313853213481314440115217414941565000142541485005000False
4450000MHigh School DiplomaSingle37222322460044597648953488514931851143100040351000140028000True
55210000MHigh School DiplomaMarried45234456115785122904129847137277145533154105104781047811078110781167810478True
66130000MHigh School DiplomaMarried56122223646176597867282685577279671345300030003000550000True
77200000FHigh School DiplomaSingle47222222199436202947193936196186200162189915821470006800713406836True
8820000FBScSingle3115443221703210872146120835202192048701000007600False
9950000FMSc or PHdSingle24122222361663718837680384623922840035190014001700153216000True

Last rows

Unnamed: 0Credit LimitSexEducationMarital StatusAgePayStat/Sept05PayStat/Aug05PayStat/Jul05PayStat/Jun05PayStat/May05PayStat/Apr05Outstanding/Sept05Outstanding/Aug05Outstanding/Jul05Outstanding/Jun05Outstanding/May05Outstanding/Apr05Paid/Sept05Paid/Aug05Paid/Jul05Paid/Jun05Paid/May05Paid/Apr05Default
1324132420000MMSc or PHdSingle31322777240024002400240024002400000000True
1325132520000MBScSingle33328765145132223221936216312102620130800000000True
13261326260000MMSc or PHdSingle332222221283521317681282511424801456301501477000018000700070005500False
1327132740000MMSc or PHdMarried47222222105551108412605131021259514386100020001000020000True
13281328320000MMSc or PHdMarried482222222067322019322153922183702209772250210183008000770077000True
13291329100000MBScSingle44222222803588134484133848738261387461290050003000062004000True
1330133070000MHigh School DiplomaMarried45222222715867162371020673387076968957310030080510026000True
1331133140000MBScSingle47223222523585489253415512594715146934400002000035200True
13321332210000MBScMarried34322222250025002500250025002500000000True
1333133380000MBScSingle34222222725577770879384775198260781158700035000700004000True